{"id":"W648714500","doi":"","title":"Translink and the 2010 Olympic Winter Games","year":2011,"lang":"en","type":"article","venue":"ITE journal","topic":"Sport and Mega-Event Impacts","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Metropolitan area; Crowds; Transit (satellite); Business; Anticipation (artificial intelligence); Transport engineering; Event (particle physics); Track (disk drive); Public transport; Marketing; Computer science; Geography; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002771774,0.0002720874,0.0001133565,0.000555144,0.004615812,0.003480884,0.0003834996,0.0006194627,0.0138509],"category_scores_gemma":[0.001054578,0.0001238945,0.0001309486,0.0007257143,0.0009373624,0.0005089699,0.001460068,0.001400186,0.0007250862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01340935,"about_ca_system_score_gemma":0.008529277,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8306002,"about_ca_topic_score_gemma":0.9661409,"domain_scores_codex":[0.9996123,0.00006668104,0.000006629846,0.00002564409,0.00007699661,0.0002118008],"domain_scores_gemma":[0.9992753,0.00004191792,0.00004162461,0.000007867158,0.0001467478,0.0004865805],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0009520145,0.001192623,0.2726975,0.0004686849,0.00009354497,0.00470332,0.05374772,0.002241377,0.001148627,0.02642117,0.4321347,0.2041986],"study_design_scores_gemma":[0.0000254947,0.0001515467,0.545163,0.0002768173,0.00002051859,0.0001653662,0.1009679,0.00039086,0.0001953348,0.001433623,0.351166,0.0000436322],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6209818,0.002981316,0.0001882019,0.02366923,0.0007241942,0.00007639094,0.001159941,0.00004662573,0.3501722],"genre_scores_gemma":[0.9243242,0.002542537,0.0001295678,0.001591694,0.0001298302,0.0000389252,0.0006794682,0.00002481027,0.07053907],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1693998,"threshold_uncertainty_score":0.3407948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05246702359378223,"score_gpt":0.2927112137591018,"score_spread":0.2402441901653196,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}